Generally, the output space \(W\)  has to be defined before the training phase. The user needs to
define the shape of the grid — square or hexagonal —, its size, and whether
the map wraps around (toroidal maps).
Each neuron \(i\)  from the model has an associated weight vector with the same dimensionality
as the input space, \(w_{i}(t)\) , where \(t\)  corresponds to the epoch of the training stage.
The initial values \(w_i(t_0)\)  can be assigned randomly, or based on
Principal Component Analysis [1]}.
